PGM-MVS | | | 98.86 28 | 99.35 23 | 98.29 31 | 99.77 1 | 99.63 29 | 99.67 6 | 95.63 40 | 98.66 105 | 95.27 46 | 99.11 23 | 99.82 37 | 99.67 4 | 99.33 21 | 99.19 20 | 99.73 57 | 99.74 69 |
|
SMA-MVS | | | 99.38 2 | 99.60 2 | 99.12 6 | 99.76 2 | 99.62 33 | 99.39 27 | 98.23 14 | 99.52 14 | 98.03 12 | 99.45 8 | 99.98 1 | 99.64 5 | 99.58 6 | 99.30 11 | 99.68 92 | 99.76 54 |
|
CSCG | | | 98.90 27 | 98.93 46 | 98.85 21 | 99.75 3 | 99.72 4 | 99.49 18 | 96.58 37 | 99.38 20 | 98.05 11 | 98.97 29 | 97.87 64 | 99.49 18 | 97.78 120 | 98.92 31 | 99.78 37 | 99.90 3 |
|
APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 1 | 99.74 4 | 99.74 3 | 99.75 1 | 98.34 2 | 99.56 9 | 98.72 3 | 99.57 4 | 99.97 4 | 99.53 15 | 99.65 2 | 99.25 14 | 99.84 5 | 99.77 50 |
|
ACMMP_Plus | | | 99.05 22 | 99.45 9 | 98.58 27 | 99.73 5 | 99.60 42 | 99.64 8 | 98.28 10 | 99.23 43 | 94.57 60 | 99.35 12 | 99.97 4 | 99.55 13 | 99.63 3 | 98.66 45 | 99.70 81 | 99.74 69 |
|
zzz-MVS | | | 99.31 4 | 99.44 12 | 99.16 4 | 99.73 5 | 99.65 20 | 99.63 10 | 98.26 11 | 99.27 35 | 98.01 13 | 99.27 14 | 99.97 4 | 99.60 7 | 99.59 5 | 98.58 51 | 99.71 72 | 99.73 73 |
|
ESAPD | | | 99.23 11 | 99.41 16 | 99.01 15 | 99.70 7 | 99.69 11 | 99.40 26 | 98.31 5 | 98.94 77 | 97.70 18 | 99.40 10 | 99.97 4 | 99.17 42 | 99.54 8 | 98.67 44 | 99.78 37 | 99.67 109 |
|
HFP-MVS | | | 99.32 3 | 99.53 5 | 99.07 10 | 99.69 8 | 99.59 44 | 99.63 10 | 98.31 5 | 99.56 9 | 97.37 22 | 99.27 14 | 99.97 4 | 99.70 3 | 99.35 19 | 99.24 16 | 99.71 72 | 99.76 54 |
|
HPM-MVS++ | | | 99.10 18 | 99.30 24 | 98.86 20 | 99.69 8 | 99.48 59 | 99.59 13 | 98.34 2 | 99.26 38 | 96.55 33 | 99.10 24 | 99.96 10 | 99.36 26 | 99.25 24 | 98.37 65 | 99.64 122 | 99.66 118 |
|
APD-MVS | | | 99.25 9 | 99.38 18 | 99.09 8 | 99.69 8 | 99.58 46 | 99.56 14 | 98.32 4 | 98.85 84 | 97.87 15 | 98.91 36 | 99.92 25 | 99.30 31 | 99.45 13 | 99.38 8 | 99.79 34 | 99.58 134 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HSP-MVS | | | 99.31 4 | 99.43 14 | 99.17 2 | 99.68 11 | 99.75 2 | 99.72 2 | 98.31 5 | 99.45 17 | 98.16 9 | 99.28 13 | 99.98 1 | 99.30 31 | 99.34 20 | 98.41 60 | 99.81 26 | 99.81 31 |
|
X-MVS | | | 98.93 26 | 99.37 19 | 98.42 28 | 99.67 12 | 99.62 33 | 99.60 12 | 98.15 19 | 99.08 61 | 93.81 81 | 98.46 54 | 99.95 15 | 99.59 9 | 99.49 11 | 99.21 19 | 99.68 92 | 99.75 65 |
|
MCST-MVS | | | 99.11 17 | 99.27 26 | 98.93 18 | 99.67 12 | 99.33 80 | 99.51 17 | 98.31 5 | 99.28 33 | 96.57 32 | 99.10 24 | 99.90 28 | 99.71 2 | 99.19 25 | 98.35 67 | 99.82 13 | 99.71 89 |
|
ACMMPR | | | 99.30 6 | 99.54 4 | 99.03 13 | 99.66 14 | 99.64 25 | 99.68 5 | 98.25 12 | 99.56 9 | 97.12 26 | 99.19 17 | 99.95 15 | 99.72 1 | 99.43 14 | 99.25 14 | 99.72 62 | 99.77 50 |
|
SteuartSystems-ACMMP | | | 99.20 13 | 99.51 6 | 98.83 23 | 99.66 14 | 99.66 19 | 99.71 4 | 98.12 23 | 99.14 53 | 96.62 30 | 99.16 19 | 99.98 1 | 99.12 50 | 99.63 3 | 99.19 20 | 99.78 37 | 99.83 24 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 99.23 11 | 99.28 25 | 99.17 2 | 99.65 16 | 99.34 78 | 99.46 21 | 98.21 15 | 99.28 33 | 98.47 5 | 98.89 38 | 99.94 23 | 99.50 16 | 99.42 15 | 98.61 48 | 99.73 57 | 99.52 145 |
|
MP-MVS | | | 99.07 20 | 99.36 20 | 98.74 24 | 99.63 17 | 99.57 48 | 99.66 7 | 98.25 12 | 99.00 72 | 95.62 39 | 98.97 29 | 99.94 23 | 99.54 14 | 99.51 10 | 98.79 41 | 99.71 72 | 99.73 73 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
NCCC | | | 99.05 22 | 99.08 34 | 99.02 14 | 99.62 18 | 99.38 71 | 99.43 25 | 98.21 15 | 99.36 24 | 97.66 19 | 97.79 73 | 99.90 28 | 99.45 21 | 99.17 26 | 98.43 58 | 99.77 42 | 99.51 149 |
|
CP-MVS | | | 99.27 7 | 99.44 12 | 99.08 9 | 99.62 18 | 99.58 46 | 99.53 15 | 98.16 17 | 99.21 46 | 97.79 16 | 99.15 20 | 99.96 10 | 99.59 9 | 99.54 8 | 98.86 36 | 99.78 37 | 99.74 69 |
|
AdaColmap | | | 99.06 21 | 98.98 44 | 99.15 5 | 99.60 20 | 99.30 84 | 99.38 28 | 98.16 17 | 99.02 71 | 98.55 4 | 98.71 44 | 99.57 49 | 99.58 12 | 99.09 30 | 97.84 95 | 99.64 122 | 99.36 162 |
|
CPTT-MVS | | | 99.14 16 | 99.20 29 | 99.06 11 | 99.58 21 | 99.53 53 | 99.45 22 | 97.80 31 | 99.19 49 | 98.32 8 | 98.58 48 | 99.95 15 | 99.60 7 | 99.28 23 | 98.20 78 | 99.64 122 | 99.69 97 |
|
QAPM | | | 98.62 37 | 99.04 40 | 98.13 35 | 99.57 22 | 99.48 59 | 99.17 36 | 94.78 50 | 99.57 8 | 96.16 35 | 96.73 99 | 99.80 38 | 99.33 28 | 98.79 49 | 99.29 13 | 99.75 46 | 99.64 125 |
|
3Dnovator | | 96.92 7 | 98.67 34 | 99.05 37 | 98.23 34 | 99.57 22 | 99.45 63 | 99.11 39 | 94.66 53 | 99.69 3 | 96.80 29 | 96.55 107 | 99.61 46 | 99.40 24 | 98.87 45 | 99.49 3 | 99.85 4 | 99.66 118 |
|
DeepC-MVS_fast | | 98.34 1 | 99.17 14 | 99.45 9 | 98.85 21 | 99.55 24 | 99.37 73 | 99.64 8 | 98.05 26 | 99.53 12 | 96.58 31 | 98.93 31 | 99.92 25 | 99.49 18 | 99.46 12 | 99.32 10 | 99.80 33 | 99.64 125 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | | | | 99.53 25 | | | | | | | 99.89 30 | | | | | |
|
3Dnovator+ | | 96.92 7 | 98.71 33 | 99.05 37 | 98.32 30 | 99.53 25 | 99.34 78 | 99.06 43 | 94.61 54 | 99.65 4 | 97.49 20 | 96.75 97 | 99.86 33 | 99.44 22 | 98.78 50 | 99.30 11 | 99.81 26 | 99.67 109 |
|
MSLP-MVS++ | | | 99.15 15 | 99.24 27 | 99.04 12 | 99.52 27 | 99.49 58 | 99.09 41 | 98.07 25 | 99.37 22 | 98.47 5 | 97.79 73 | 99.89 30 | 99.50 16 | 98.93 39 | 99.45 4 | 99.61 137 | 99.76 54 |
|
OpenMVS | | 96.23 11 | 97.95 50 | 98.45 58 | 97.35 47 | 99.52 27 | 99.42 67 | 98.91 49 | 94.61 54 | 98.87 81 | 92.24 99 | 94.61 140 | 99.05 54 | 99.10 53 | 98.64 62 | 99.05 24 | 99.74 51 | 99.51 149 |
|
PLC | | 97.93 2 | 99.02 25 | 98.94 45 | 99.11 7 | 99.46 29 | 99.24 92 | 99.06 43 | 97.96 28 | 99.31 30 | 99.16 1 | 97.90 71 | 99.79 40 | 99.36 26 | 98.71 56 | 98.12 81 | 99.65 111 | 99.52 145 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVS_111021_HR | | | 98.59 38 | 99.36 20 | 97.68 43 | 99.42 30 | 99.61 38 | 98.14 84 | 94.81 49 | 99.31 30 | 95.00 52 | 99.51 6 | 99.79 40 | 99.00 60 | 98.94 38 | 98.83 38 | 99.69 83 | 99.57 139 |
|
OMC-MVS | | | 98.84 29 | 99.01 43 | 98.65 26 | 99.39 31 | 99.23 93 | 99.22 33 | 96.70 36 | 99.40 19 | 97.77 17 | 97.89 72 | 99.80 38 | 99.21 35 | 99.02 34 | 98.65 46 | 99.57 158 | 99.07 178 |
|
TSAR-MVS + ACMM | | | 98.77 30 | 99.45 9 | 97.98 39 | 99.37 32 | 99.46 61 | 99.44 24 | 98.13 22 | 99.65 4 | 92.30 98 | 98.91 36 | 99.95 15 | 99.05 56 | 99.42 15 | 98.95 29 | 99.58 154 | 99.82 26 |
|
MVS_111021_LR | | | 98.67 34 | 99.41 16 | 97.81 42 | 99.37 32 | 99.53 53 | 98.51 60 | 95.52 42 | 99.27 35 | 94.85 55 | 99.56 5 | 99.69 44 | 99.04 57 | 99.36 18 | 98.88 34 | 99.60 144 | 99.58 134 |
|
train_agg | | | 98.73 32 | 99.11 32 | 98.28 32 | 99.36 34 | 99.35 76 | 99.48 20 | 97.96 28 | 98.83 88 | 93.86 80 | 98.70 45 | 99.86 33 | 99.44 22 | 99.08 32 | 98.38 63 | 99.61 137 | 99.58 134 |
|
ACMMP | | | 98.74 31 | 99.03 41 | 98.40 29 | 99.36 34 | 99.64 25 | 99.20 34 | 97.75 32 | 98.82 90 | 95.24 47 | 98.85 39 | 99.87 32 | 99.17 42 | 98.74 55 | 97.50 113 | 99.71 72 | 99.76 54 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
MAR-MVS | | | 97.71 56 | 98.04 77 | 97.32 48 | 99.35 36 | 98.91 108 | 97.65 103 | 91.68 104 | 98.00 133 | 97.01 27 | 97.72 77 | 94.83 95 | 98.85 64 | 98.44 76 | 98.86 36 | 99.41 181 | 99.52 145 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
abl_6 | | | | | 98.09 36 | 99.33 37 | 99.22 94 | 98.79 53 | 94.96 48 | 98.52 114 | 97.00 28 | 97.30 83 | 99.86 33 | 98.76 65 | | | 99.69 83 | 99.41 159 |
|
CDPH-MVS | | | 98.41 40 | 99.10 33 | 97.61 45 | 99.32 38 | 99.36 74 | 99.49 18 | 96.15 39 | 98.82 90 | 91.82 101 | 98.41 55 | 99.66 45 | 99.10 53 | 98.93 39 | 98.97 28 | 99.75 46 | 99.58 134 |
|
CNLPA | | | 99.03 24 | 99.05 37 | 99.01 15 | 99.27 39 | 99.22 94 | 99.03 45 | 97.98 27 | 99.34 28 | 99.00 2 | 98.25 61 | 99.71 43 | 99.31 30 | 98.80 48 | 98.82 39 | 99.48 171 | 99.17 171 |
|
MSDG | | | 98.27 44 | 98.29 66 | 98.24 33 | 99.20 40 | 99.22 94 | 99.20 34 | 97.82 30 | 99.37 22 | 94.43 67 | 95.90 121 | 97.31 70 | 99.12 50 | 98.76 52 | 98.35 67 | 99.67 100 | 99.14 175 |
|
PHI-MVS | | | 99.08 19 | 99.43 14 | 98.67 25 | 99.15 41 | 99.59 44 | 99.11 39 | 97.35 34 | 99.14 53 | 97.30 23 | 99.44 9 | 99.96 10 | 99.32 29 | 98.89 43 | 99.39 7 | 99.79 34 | 99.58 134 |
|
PatchMatch-RL | | | 97.77 54 | 98.25 67 | 97.21 53 | 99.11 42 | 99.25 90 | 97.06 125 | 94.09 65 | 98.72 103 | 95.14 49 | 98.47 53 | 96.29 80 | 98.43 78 | 98.65 59 | 97.44 118 | 99.45 175 | 98.94 181 |
|
TAPA-MVS | | 97.53 5 | 98.41 40 | 98.84 50 | 97.91 40 | 99.08 43 | 99.33 80 | 99.15 37 | 97.13 35 | 99.34 28 | 93.20 88 | 97.75 75 | 99.19 52 | 99.20 36 | 98.66 58 | 98.13 80 | 99.66 105 | 99.48 154 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
EPNet | | | 98.05 48 | 98.86 48 | 97.10 56 | 99.02 44 | 99.43 66 | 98.47 61 | 94.73 51 | 99.05 68 | 95.62 39 | 98.93 31 | 97.62 68 | 95.48 161 | 98.59 69 | 98.55 52 | 99.29 189 | 99.84 20 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet_dtu | | | 96.30 105 | 98.53 56 | 93.70 126 | 98.97 45 | 98.24 151 | 97.36 110 | 94.23 62 | 98.85 84 | 79.18 194 | 99.19 17 | 98.47 59 | 94.09 198 | 97.89 115 | 98.21 77 | 98.39 205 | 98.85 187 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
COLMAP_ROB | | 96.15 12 | 97.78 53 | 98.17 72 | 97.32 48 | 98.84 46 | 99.45 63 | 99.28 31 | 95.43 43 | 99.48 16 | 91.80 102 | 94.83 138 | 98.36 61 | 98.90 61 | 98.09 99 | 97.85 94 | 99.68 92 | 99.15 172 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepPCF-MVS | | 97.74 3 | 98.34 42 | 99.46 8 | 97.04 60 | 98.82 47 | 99.33 80 | 96.28 139 | 97.47 33 | 99.58 7 | 94.70 59 | 98.99 28 | 99.85 36 | 97.24 111 | 99.55 7 | 99.34 9 | 97.73 215 | 99.56 140 |
|
SD-MVS | | | 99.25 9 | 99.50 7 | 98.96 17 | 98.79 48 | 99.55 51 | 99.33 30 | 98.29 9 | 99.75 1 | 97.96 14 | 99.15 20 | 99.95 15 | 99.61 6 | 99.17 26 | 99.06 23 | 99.81 26 | 99.84 20 |
|
TSAR-MVS + MP. | | | 99.27 7 | 99.57 3 | 98.92 19 | 98.78 49 | 99.53 53 | 99.72 2 | 98.11 24 | 99.73 2 | 97.43 21 | 99.15 20 | 99.96 10 | 99.59 9 | 99.73 1 | 99.07 22 | 99.88 1 | 99.82 26 |
|
PCF-MVS | | 97.50 6 | 98.18 46 | 98.35 63 | 97.99 38 | 98.65 50 | 99.36 74 | 98.94 48 | 98.14 21 | 98.59 107 | 93.62 84 | 96.61 103 | 99.76 42 | 99.03 58 | 97.77 121 | 97.45 117 | 99.57 158 | 98.89 186 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DeepC-MVS | | 97.63 4 | 98.33 43 | 98.57 54 | 98.04 37 | 98.62 51 | 99.65 20 | 99.45 22 | 98.15 19 | 99.51 15 | 92.80 94 | 95.74 126 | 96.44 78 | 99.46 20 | 99.37 17 | 99.50 2 | 99.78 37 | 99.81 31 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CANet | | | 98.46 39 | 99.16 30 | 97.64 44 | 98.48 52 | 99.64 25 | 99.35 29 | 94.71 52 | 99.53 12 | 95.17 48 | 97.63 79 | 99.59 47 | 98.38 80 | 98.88 44 | 98.99 27 | 99.74 51 | 99.86 16 |
|
LS3D | | | 97.79 52 | 98.25 67 | 97.26 52 | 98.40 53 | 99.63 29 | 99.53 15 | 98.63 1 | 99.25 40 | 88.13 124 | 96.93 95 | 94.14 109 | 99.19 38 | 99.14 28 | 99.23 17 | 99.69 83 | 99.42 158 |
|
CHOSEN 280x420 | | | 97.99 49 | 99.24 27 | 96.53 81 | 98.34 54 | 99.61 38 | 98.36 74 | 89.80 143 | 99.27 35 | 95.08 50 | 99.81 1 | 98.58 57 | 98.64 71 | 99.02 34 | 98.92 31 | 98.93 197 | 99.48 154 |
|
DELS-MVS | | | 98.19 45 | 98.77 51 | 97.52 46 | 98.29 55 | 99.71 8 | 99.12 38 | 94.58 57 | 98.80 93 | 95.38 45 | 96.24 113 | 98.24 62 | 97.92 95 | 99.06 33 | 99.52 1 | 99.82 13 | 99.79 41 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
RPSCF | | | 97.61 59 | 98.16 73 | 96.96 72 | 98.10 56 | 99.00 101 | 98.84 51 | 93.76 82 | 99.45 17 | 94.78 58 | 99.39 11 | 99.31 51 | 98.53 76 | 96.61 154 | 95.43 164 | 97.74 213 | 97.93 203 |
|
PVSNet_BlendedMVS | | | 97.51 63 | 97.71 87 | 97.28 50 | 98.06 57 | 99.61 38 | 97.31 112 | 95.02 46 | 99.08 61 | 95.51 42 | 98.05 65 | 90.11 130 | 98.07 91 | 98.91 41 | 98.40 61 | 99.72 62 | 99.78 43 |
|
PVSNet_Blended | | | 97.51 63 | 97.71 87 | 97.28 50 | 98.06 57 | 99.61 38 | 97.31 112 | 95.02 46 | 99.08 61 | 95.51 42 | 98.05 65 | 90.11 130 | 98.07 91 | 98.91 41 | 98.40 61 | 99.72 62 | 99.78 43 |
|
MVS_0304 | | | 98.14 47 | 99.03 41 | 97.10 56 | 98.05 59 | 99.63 29 | 99.27 32 | 94.33 59 | 99.63 6 | 93.06 91 | 97.32 82 | 99.05 54 | 98.09 90 | 98.82 47 | 98.87 35 | 99.81 26 | 99.89 7 |
|
CHOSEN 1792x2688 | | | 96.41 100 | 96.99 110 | 95.74 101 | 98.01 60 | 99.72 4 | 97.70 102 | 90.78 123 | 99.13 57 | 90.03 117 | 87.35 207 | 95.36 90 | 98.33 82 | 98.59 69 | 98.91 33 | 99.59 150 | 99.87 13 |
|
HyFIR lowres test | | | 95.99 112 | 96.56 117 | 95.32 106 | 97.99 61 | 99.65 20 | 96.54 133 | 88.86 151 | 98.44 116 | 89.77 120 | 84.14 219 | 97.05 73 | 99.03 58 | 98.55 71 | 98.19 79 | 99.73 57 | 99.86 16 |
|
OPM-MVS | | | 96.22 107 | 95.85 141 | 96.65 78 | 97.75 62 | 98.54 133 | 99.00 47 | 95.53 41 | 96.88 182 | 89.88 118 | 95.95 120 | 86.46 152 | 98.07 91 | 97.65 129 | 96.63 134 | 99.67 100 | 98.83 188 |
|
tmp_tt | | | | | 82.25 223 | 97.73 63 | 88.71 235 | 80.18 230 | 68.65 238 | 99.15 51 | 86.98 132 | 99.47 7 | 85.31 163 | 68.35 235 | 87.51 230 | 83.81 231 | 91.64 234 | |
|
TSAR-MVS + COLMAP | | | 96.79 84 | 96.55 118 | 97.06 59 | 97.70 64 | 98.46 136 | 99.07 42 | 96.23 38 | 99.38 20 | 91.32 107 | 98.80 40 | 85.61 159 | 98.69 69 | 97.64 130 | 96.92 128 | 99.37 184 | 99.06 179 |
|
PVSNet_Blended_VisFu | | | 97.41 65 | 98.49 57 | 96.15 88 | 97.49 65 | 99.76 1 | 96.02 142 | 93.75 84 | 99.26 38 | 93.38 87 | 93.73 146 | 99.35 50 | 96.47 134 | 98.96 36 | 98.46 56 | 99.77 42 | 99.90 3 |
|
MS-PatchMatch | | | 95.99 112 | 97.26 104 | 94.51 113 | 97.46 66 | 98.76 118 | 97.27 114 | 86.97 173 | 99.09 59 | 89.83 119 | 93.51 149 | 97.78 65 | 96.18 139 | 97.53 134 | 95.71 161 | 99.35 185 | 98.41 194 |
|
XVS | | | | | | 97.42 67 | 99.62 33 | 98.59 58 | | | 93.81 81 | | 99.95 15 | | | | 99.69 83 | |
|
X-MVStestdata | | | | | | 97.42 67 | 99.62 33 | 98.59 58 | | | 93.81 81 | | 99.95 15 | | | | 99.69 83 | |
|
LGP-MVS_train | | | 96.23 106 | 96.89 112 | 95.46 105 | 97.32 69 | 98.77 116 | 98.81 52 | 93.60 85 | 98.58 108 | 85.52 140 | 99.08 26 | 86.67 149 | 97.83 101 | 97.87 116 | 97.51 112 | 99.69 83 | 99.73 73 |
|
CMPMVS | | 70.31 18 | 90.74 208 | 91.06 217 | 90.36 198 | 97.32 69 | 97.43 194 | 92.97 204 | 87.82 167 | 93.50 224 | 75.34 212 | 83.27 222 | 84.90 167 | 92.19 212 | 92.64 219 | 91.21 227 | 96.50 229 | 94.46 224 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
HQP-MVS | | | 96.37 101 | 96.58 116 | 96.13 90 | 97.31 71 | 98.44 139 | 98.45 62 | 95.22 44 | 98.86 82 | 88.58 122 | 98.33 59 | 87.00 141 | 97.67 102 | 97.23 142 | 96.56 137 | 99.56 161 | 99.62 128 |
|
ACMM | | 96.26 9 | 96.67 95 | 96.69 115 | 96.66 77 | 97.29 72 | 98.46 136 | 96.48 136 | 95.09 45 | 99.21 46 | 93.19 89 | 98.78 42 | 86.73 148 | 98.17 87 | 97.84 118 | 96.32 143 | 99.74 51 | 99.49 153 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 97.13 75 | 99.14 31 | 94.78 110 | 97.21 73 | 99.38 71 | 97.56 104 | 92.04 97 | 98.48 115 | 88.03 125 | 98.39 57 | 99.91 27 | 94.03 199 | 99.33 21 | 99.23 17 | 99.81 26 | 99.25 167 |
|
UGNet | | | 97.66 58 | 99.07 36 | 96.01 94 | 97.19 74 | 99.65 20 | 97.09 123 | 93.39 88 | 99.35 25 | 94.40 69 | 98.79 41 | 99.59 47 | 94.24 196 | 98.04 108 | 98.29 74 | 99.73 57 | 99.80 34 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
TSAR-MVS + GP. | | | 98.66 36 | 99.36 20 | 97.85 41 | 97.16 75 | 99.46 61 | 99.03 45 | 94.59 56 | 99.09 59 | 97.19 25 | 99.73 3 | 99.95 15 | 99.39 25 | 98.95 37 | 98.69 43 | 99.75 46 | 99.65 121 |
|
CANet_DTU | | | 96.64 96 | 99.08 34 | 93.81 122 | 97.10 76 | 99.42 67 | 98.85 50 | 90.01 137 | 99.31 30 | 79.98 180 | 99.78 2 | 99.10 53 | 97.42 108 | 98.35 79 | 98.05 85 | 99.47 173 | 99.53 143 |
|
IB-MVS | | 93.96 15 | 95.02 130 | 96.44 130 | 93.36 136 | 97.05 77 | 99.28 87 | 90.43 215 | 93.39 88 | 98.02 132 | 96.02 36 | 94.92 137 | 92.07 124 | 83.52 226 | 95.38 181 | 95.82 157 | 99.72 62 | 99.59 132 |
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021 |
ACMP | | 96.25 10 | 96.62 98 | 96.72 114 | 96.50 84 | 96.96 78 | 98.75 119 | 97.80 99 | 94.30 60 | 98.85 84 | 93.12 90 | 98.78 42 | 86.61 150 | 97.23 112 | 97.73 124 | 96.61 135 | 99.62 134 | 99.71 89 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 95.42 14 | 95.27 127 | 95.96 137 | 94.45 114 | 96.83 79 | 98.78 115 | 94.72 182 | 91.67 105 | 98.95 74 | 86.82 134 | 96.42 110 | 83.67 176 | 97.00 116 | 97.48 136 | 96.68 133 | 99.69 83 | 99.76 54 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CLD-MVS | | | 96.74 88 | 96.51 121 | 97.01 67 | 96.71 80 | 98.62 128 | 98.73 54 | 94.38 58 | 98.94 77 | 94.46 66 | 97.33 81 | 87.03 140 | 98.07 91 | 97.20 144 | 96.87 129 | 99.72 62 | 99.54 142 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TDRefinement | | | 93.04 166 | 93.57 192 | 92.41 146 | 96.58 81 | 98.77 116 | 97.78 101 | 91.96 100 | 98.12 128 | 80.84 166 | 89.13 184 | 79.87 215 | 87.78 218 | 96.44 160 | 94.50 203 | 99.54 166 | 98.15 198 |
|
Anonymous202405211 | | | | 97.40 97 | | 96.45 82 | 99.54 52 | 98.08 88 | 93.79 81 | 98.24 123 | | 93.55 147 | 94.41 102 | 98.88 63 | 98.04 108 | 98.24 76 | 99.75 46 | 99.76 54 |
|
Anonymous20240521 | | | 97.56 61 | 98.36 62 | 96.62 80 | 96.44 83 | 98.36 146 | 98.37 72 | 91.73 103 | 99.11 58 | 94.80 57 | 98.36 58 | 96.28 81 | 98.60 73 | 98.12 95 | 98.44 57 | 99.76 44 | 99.87 13 |
|
ACMH+ | | 95.51 13 | 95.40 122 | 96.00 135 | 94.70 111 | 96.33 84 | 98.79 113 | 96.79 128 | 91.32 113 | 98.77 99 | 87.18 131 | 95.60 131 | 85.46 161 | 96.97 117 | 97.15 145 | 96.59 136 | 99.59 150 | 99.65 121 |
|
tfpn1000 | | | 97.60 60 | 98.21 70 | 96.89 74 | 96.32 85 | 99.60 42 | 97.99 93 | 93.85 78 | 99.21 46 | 95.03 51 | 98.49 51 | 93.69 113 | 98.31 83 | 98.50 74 | 98.31 73 | 99.86 2 | 99.70 91 |
|
Anonymous20231211 | | | 97.10 76 | 97.06 108 | 97.14 54 | 96.32 85 | 99.52 56 | 98.16 83 | 93.76 82 | 98.84 87 | 95.98 37 | 90.92 163 | 94.58 101 | 98.90 61 | 97.72 125 | 98.10 82 | 99.71 72 | 99.75 65 |
|
tfpn111 | | | 96.96 81 | 96.91 111 | 97.03 61 | 96.31 87 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 158 | 94.50 63 | 98.65 46 | 86.93 142 | 99.14 45 | 98.26 84 | 97.80 98 | 99.82 13 | 99.70 91 |
|
tfpn_ndepth | | | 97.71 56 | 98.30 65 | 97.02 65 | 96.31 87 | 99.56 49 | 98.05 90 | 93.94 76 | 98.95 74 | 95.59 41 | 98.40 56 | 94.79 97 | 98.39 79 | 98.40 78 | 98.42 59 | 99.86 2 | 99.56 140 |
|
conf200view11 | | | 96.75 86 | 96.51 121 | 97.03 61 | 96.31 87 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 158 | 94.50 63 | 95.90 121 | 86.93 142 | 99.14 45 | 98.26 84 | 97.80 98 | 99.82 13 | 99.70 91 |
|
thres100view900 | | | 96.72 89 | 96.47 125 | 97.00 69 | 96.31 87 | 99.52 56 | 98.28 79 | 94.01 66 | 97.35 158 | 94.52 61 | 95.90 121 | 86.93 142 | 99.09 55 | 98.07 102 | 97.87 93 | 99.81 26 | 99.63 127 |
|
tfpn200view9 | | | 96.75 86 | 96.51 121 | 97.03 61 | 96.31 87 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 158 | 94.52 61 | 95.90 121 | 86.93 142 | 99.14 45 | 98.26 84 | 97.80 98 | 99.82 13 | 99.70 91 |
|
thres200 | | | 96.76 85 | 96.53 119 | 97.03 61 | 96.31 87 | 99.67 13 | 98.37 72 | 93.99 68 | 97.68 153 | 94.49 65 | 95.83 125 | 86.77 147 | 99.18 40 | 98.26 84 | 97.82 97 | 99.82 13 | 99.66 118 |
|
conf0.01 | | | 96.35 102 | 95.71 142 | 97.10 56 | 96.30 93 | 99.65 20 | 98.41 64 | 94.10 64 | 97.35 158 | 94.82 56 | 95.44 134 | 81.88 203 | 99.14 45 | 98.16 93 | 97.80 98 | 99.82 13 | 99.69 97 |
|
conf0.002 | | | 96.31 104 | 95.63 144 | 97.11 55 | 96.29 94 | 99.64 25 | 98.41 64 | 94.11 63 | 97.35 158 | 94.86 54 | 95.49 133 | 81.06 208 | 99.14 45 | 98.14 94 | 98.02 87 | 99.82 13 | 99.69 97 |
|
view800 | | | 96.70 91 | 96.45 128 | 96.99 71 | 96.29 94 | 99.69 11 | 98.39 71 | 93.95 75 | 97.92 140 | 94.25 73 | 96.23 114 | 85.57 160 | 99.22 33 | 98.28 82 | 97.71 104 | 99.82 13 | 99.76 54 |
|
tfpn | | | 96.22 107 | 95.62 145 | 96.93 73 | 96.29 94 | 99.72 4 | 98.34 76 | 93.94 76 | 97.96 137 | 93.94 76 | 96.45 109 | 79.09 218 | 99.22 33 | 98.28 82 | 98.06 84 | 99.83 9 | 99.78 43 |
|
view600 | | | 96.70 91 | 96.44 130 | 97.01 67 | 96.28 97 | 99.67 13 | 98.42 63 | 93.99 68 | 97.87 143 | 94.34 71 | 95.99 118 | 85.94 156 | 99.20 36 | 98.26 84 | 97.64 106 | 99.82 13 | 99.73 73 |
|
thres600view7 | | | 96.69 93 | 96.43 132 | 97.00 69 | 96.28 97 | 99.67 13 | 98.41 64 | 93.99 68 | 97.85 146 | 94.29 72 | 95.96 119 | 85.91 157 | 99.19 38 | 98.26 84 | 97.63 107 | 99.82 13 | 99.73 73 |
|
thres400 | | | 96.71 90 | 96.45 128 | 97.02 65 | 96.28 97 | 99.63 29 | 98.41 64 | 94.00 67 | 97.82 148 | 94.42 68 | 95.74 126 | 86.26 153 | 99.18 40 | 98.20 91 | 97.79 102 | 99.81 26 | 99.70 91 |
|
canonicalmvs | | | 97.31 71 | 97.81 85 | 96.72 75 | 96.20 100 | 99.45 63 | 98.21 80 | 91.60 106 | 99.22 44 | 95.39 44 | 98.48 52 | 90.95 128 | 99.16 44 | 97.66 127 | 99.05 24 | 99.76 44 | 99.90 3 |
|
conf0.05thres1000 | | | 96.34 103 | 96.47 125 | 96.17 87 | 96.16 101 | 99.71 8 | 97.82 97 | 93.46 86 | 98.10 129 | 90.69 109 | 96.75 97 | 85.26 164 | 99.11 52 | 98.05 106 | 97.65 105 | 99.82 13 | 99.80 34 |
|
thresconf0.02 | | | 97.18 73 | 97.81 85 | 96.45 85 | 96.11 102 | 99.20 97 | 98.21 80 | 94.26 61 | 99.14 53 | 91.72 103 | 98.65 46 | 91.51 127 | 98.57 74 | 98.22 90 | 98.47 55 | 99.82 13 | 99.50 151 |
|
tfpn_n400 | | | 97.32 68 | 98.38 60 | 96.09 91 | 96.07 103 | 99.30 84 | 98.00 91 | 93.84 79 | 99.35 25 | 90.50 112 | 98.93 31 | 94.24 106 | 98.30 84 | 98.65 59 | 98.60 49 | 99.83 9 | 99.60 130 |
|
tfpnconf | | | 97.32 68 | 98.38 60 | 96.09 91 | 96.07 103 | 99.30 84 | 98.00 91 | 93.84 79 | 99.35 25 | 90.50 112 | 98.93 31 | 94.24 106 | 98.30 84 | 98.65 59 | 98.60 49 | 99.83 9 | 99.60 130 |
|
tfpnview11 | | | 97.32 68 | 98.33 64 | 96.14 89 | 96.07 103 | 99.31 83 | 98.08 88 | 93.96 74 | 99.25 40 | 90.50 112 | 98.93 31 | 94.24 106 | 98.38 80 | 98.61 65 | 98.36 66 | 99.84 5 | 99.59 132 |
|
IS_MVSNet | | | 97.86 51 | 98.86 48 | 96.68 76 | 96.02 106 | 99.72 4 | 98.35 75 | 93.37 90 | 98.75 102 | 94.01 74 | 96.88 96 | 98.40 60 | 98.48 77 | 99.09 30 | 99.42 5 | 99.83 9 | 99.80 34 |
|
USDC | | | 94.26 143 | 94.83 155 | 93.59 128 | 96.02 106 | 98.44 139 | 97.84 96 | 88.65 155 | 98.86 82 | 82.73 158 | 94.02 143 | 80.56 209 | 96.76 124 | 97.28 141 | 96.15 150 | 99.55 162 | 98.50 192 |
|
FC-MVSNet-train | | | 97.04 77 | 97.91 83 | 96.03 93 | 96.00 108 | 98.41 142 | 96.53 135 | 93.42 87 | 99.04 70 | 93.02 92 | 98.03 67 | 94.32 104 | 97.47 107 | 97.93 113 | 97.77 103 | 99.75 46 | 99.88 11 |
|
Vis-MVSNet (Re-imp) | | | 97.40 66 | 98.89 47 | 95.66 103 | 95.99 109 | 99.62 33 | 97.82 97 | 93.22 91 | 98.82 90 | 91.40 106 | 96.94 94 | 98.56 58 | 95.70 150 | 99.14 28 | 99.41 6 | 99.79 34 | 99.75 65 |
|
MVSTER | | | 97.16 74 | 97.71 87 | 96.52 82 | 95.97 110 | 98.48 135 | 98.63 57 | 92.10 96 | 98.68 104 | 95.96 38 | 99.23 16 | 91.79 125 | 96.87 121 | 98.76 52 | 97.37 121 | 99.57 158 | 99.68 104 |
|
TinyColmap | | | 94.00 147 | 94.35 166 | 93.60 127 | 95.89 111 | 98.26 149 | 97.49 107 | 88.82 152 | 98.56 110 | 83.21 152 | 91.28 162 | 80.48 211 | 96.68 126 | 97.34 139 | 96.26 146 | 99.53 167 | 98.24 197 |
|
DWT-MVSNet_training | | | 95.38 123 | 95.05 151 | 95.78 98 | 95.86 112 | 98.88 109 | 97.55 105 | 90.09 136 | 98.23 124 | 96.49 34 | 97.62 80 | 86.92 146 | 97.16 113 | 92.03 223 | 94.12 205 | 97.52 218 | 97.50 206 |
|
EPMVS | | | 95.05 129 | 96.86 113 | 92.94 143 | 95.84 113 | 98.96 106 | 96.68 129 | 79.87 212 | 99.05 68 | 90.15 115 | 97.12 89 | 95.99 85 | 97.49 106 | 95.17 190 | 94.75 199 | 97.59 217 | 96.96 215 |
|
PMMVS | | | 97.52 62 | 98.39 59 | 96.51 83 | 95.82 114 | 98.73 122 | 97.80 99 | 93.05 93 | 98.76 100 | 94.39 70 | 99.07 27 | 97.03 74 | 98.55 75 | 98.31 81 | 97.61 108 | 99.43 179 | 99.21 170 |
|
casdiffmvs | | | 97.40 66 | 98.64 53 | 95.96 95 | 95.76 115 | 99.40 69 | 98.33 78 | 91.48 111 | 99.24 42 | 91.72 103 | 98.03 67 | 96.57 75 | 98.73 67 | 98.64 62 | 98.77 42 | 99.72 62 | 99.83 24 |
|
MVS_Test | | | 97.30 72 | 98.54 55 | 95.87 96 | 95.74 116 | 99.28 87 | 98.19 82 | 91.40 112 | 99.18 50 | 91.59 105 | 98.17 62 | 96.18 82 | 98.63 72 | 98.61 65 | 98.55 52 | 99.66 105 | 99.78 43 |
|
diffmvs | | | 96.92 82 | 97.86 84 | 95.82 97 | 95.70 117 | 99.28 87 | 97.98 94 | 91.13 118 | 99.08 61 | 92.48 97 | 98.09 64 | 92.81 119 | 98.18 86 | 98.11 96 | 97.83 96 | 99.44 177 | 99.81 31 |
|
tpmrst | | | 93.86 152 | 95.88 139 | 91.50 170 | 95.69 118 | 98.62 128 | 95.64 148 | 79.41 217 | 98.80 93 | 83.76 148 | 95.63 130 | 96.13 83 | 97.25 110 | 92.92 215 | 92.31 220 | 97.27 223 | 96.74 218 |
|
ADS-MVSNet | | | 94.65 136 | 97.04 109 | 91.88 163 | 95.68 119 | 98.99 103 | 95.89 143 | 79.03 221 | 99.15 51 | 85.81 139 | 96.96 93 | 98.21 63 | 97.10 114 | 94.48 209 | 94.24 204 | 97.74 213 | 97.21 211 |
|
EPP-MVSNet | | | 97.75 55 | 98.71 52 | 96.63 79 | 95.68 119 | 99.56 49 | 97.51 106 | 93.10 92 | 99.22 44 | 94.99 53 | 97.18 88 | 97.30 71 | 98.65 70 | 98.83 46 | 98.93 30 | 99.84 5 | 99.92 1 |
|
DI_MVS_plusplus_trai | | | 96.90 83 | 97.49 93 | 96.21 86 | 95.61 121 | 99.40 69 | 98.72 55 | 92.11 95 | 99.14 53 | 92.98 93 | 93.08 157 | 95.14 92 | 98.13 89 | 98.05 106 | 97.91 91 | 99.74 51 | 99.73 73 |
|
dps | | | 94.63 137 | 95.31 150 | 93.84 121 | 95.53 122 | 98.71 123 | 96.54 133 | 80.12 211 | 97.81 150 | 97.21 24 | 96.98 92 | 92.37 121 | 96.34 136 | 92.46 220 | 91.77 224 | 97.26 224 | 97.08 213 |
|
PatchmatchNet | | | 94.70 134 | 97.08 107 | 91.92 160 | 95.53 122 | 98.85 111 | 95.77 145 | 79.54 216 | 98.95 74 | 85.98 137 | 98.52 49 | 96.45 76 | 97.39 109 | 95.32 182 | 94.09 206 | 97.32 222 | 97.38 210 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
test-LLR | | | 95.50 120 | 97.32 100 | 93.37 135 | 95.49 124 | 98.74 120 | 96.44 137 | 90.82 121 | 98.18 125 | 82.75 156 | 96.60 104 | 94.67 99 | 95.54 157 | 98.09 99 | 96.00 151 | 99.20 192 | 98.93 182 |
|
test0.0.03 1 | | | 96.69 93 | 98.12 75 | 95.01 108 | 95.49 124 | 98.99 103 | 95.86 144 | 90.82 121 | 98.38 118 | 92.54 96 | 96.66 101 | 97.33 69 | 95.75 148 | 97.75 123 | 98.34 69 | 99.60 144 | 99.40 160 |
|
CostFormer | | | 94.25 144 | 94.88 154 | 93.51 132 | 95.43 126 | 98.34 147 | 96.21 140 | 80.64 208 | 97.94 139 | 94.01 74 | 98.30 60 | 86.20 155 | 97.52 104 | 92.71 216 | 92.69 216 | 97.23 226 | 98.02 202 |
|
MDTV_nov1_ep13 | | | 95.57 118 | 97.48 94 | 93.35 137 | 95.43 126 | 98.97 105 | 97.19 118 | 83.72 203 | 98.92 80 | 87.91 127 | 97.75 75 | 96.12 84 | 97.88 99 | 96.84 153 | 95.64 162 | 97.96 211 | 98.10 199 |
|
tpm cat1 | | | 94.06 145 | 94.90 153 | 93.06 140 | 95.42 128 | 98.52 134 | 96.64 131 | 80.67 207 | 97.82 148 | 92.63 95 | 93.39 151 | 95.00 93 | 96.06 143 | 91.36 227 | 91.58 226 | 96.98 227 | 96.66 220 |
|
tpmp4_e23 | | | 93.84 154 | 94.58 161 | 92.98 142 | 95.41 129 | 98.29 148 | 96.81 127 | 80.57 209 | 98.15 127 | 90.53 111 | 97.00 91 | 84.39 172 | 96.91 119 | 93.69 212 | 92.45 218 | 97.67 216 | 98.06 200 |
|
Vis-MVSNet | | | 96.16 109 | 98.22 69 | 93.75 123 | 95.33 130 | 99.70 10 | 97.27 114 | 90.85 120 | 98.30 120 | 85.51 141 | 95.72 128 | 96.45 76 | 93.69 205 | 98.70 57 | 99.00 26 | 99.84 5 | 99.69 97 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CVMVSNet | | | 95.33 126 | 97.09 106 | 93.27 138 | 95.23 131 | 98.39 144 | 95.49 151 | 92.58 94 | 97.71 152 | 83.00 155 | 94.44 142 | 93.28 116 | 93.92 202 | 97.79 119 | 98.54 54 | 99.41 181 | 99.45 156 |
|
IterMVS-LS | | | 96.12 110 | 97.48 94 | 94.53 112 | 95.19 132 | 97.56 185 | 97.15 119 | 89.19 149 | 99.08 61 | 88.23 123 | 94.97 136 | 94.73 98 | 97.84 100 | 97.86 117 | 98.26 75 | 99.60 144 | 99.88 11 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 95.81 114 | 97.31 103 | 94.06 118 | 95.09 133 | 99.35 76 | 97.24 116 | 88.22 160 | 98.54 111 | 85.38 142 | 98.52 49 | 88.68 134 | 98.70 68 | 98.32 80 | 97.93 89 | 99.74 51 | 99.84 20 |
|
testgi | | | 95.67 117 | 97.48 94 | 93.56 129 | 95.07 134 | 99.00 101 | 95.33 155 | 88.47 157 | 98.80 93 | 86.90 133 | 97.30 83 | 92.33 122 | 95.97 145 | 97.66 127 | 97.91 91 | 99.60 144 | 99.38 161 |
|
RPMNet | | | 94.66 135 | 97.16 105 | 91.75 166 | 94.98 135 | 98.59 130 | 97.00 126 | 78.37 225 | 97.98 134 | 83.78 146 | 96.27 112 | 94.09 111 | 96.91 119 | 97.36 138 | 96.73 131 | 99.48 171 | 99.09 177 |
|
LP | | | 92.12 199 | 94.60 159 | 89.22 206 | 94.96 136 | 98.45 138 | 93.01 203 | 77.58 226 | 97.85 146 | 77.26 203 | 89.80 178 | 93.00 118 | 94.54 189 | 93.69 212 | 92.58 217 | 98.00 210 | 96.83 217 |
|
CR-MVSNet | | | 94.57 140 | 97.34 99 | 91.33 174 | 94.90 137 | 98.59 130 | 97.15 119 | 79.14 219 | 97.98 134 | 80.42 173 | 96.59 106 | 93.50 115 | 96.85 122 | 98.10 97 | 97.49 114 | 99.50 170 | 99.15 172 |
|
gg-mvs-nofinetune | | | 90.85 207 | 94.14 168 | 87.02 213 | 94.89 138 | 99.25 90 | 98.64 56 | 76.29 230 | 88.24 232 | 57.50 234 | 79.93 227 | 95.45 89 | 95.18 183 | 98.77 51 | 98.07 83 | 99.62 134 | 99.24 168 |
|
IterMVS | | | 94.81 133 | 97.71 87 | 91.42 172 | 94.83 139 | 97.63 178 | 97.38 109 | 85.08 188 | 98.93 79 | 75.67 209 | 94.02 143 | 97.64 66 | 96.66 128 | 98.45 75 | 97.60 109 | 98.90 198 | 99.72 85 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 93.96 149 | 97.36 98 | 90.00 201 | 94.76 140 | 98.65 126 | 90.11 218 | 78.57 224 | 97.96 137 | 80.42 173 | 96.07 116 | 94.10 110 | 96.85 122 | 98.10 97 | 97.49 114 | 99.26 190 | 99.15 172 |
|
CDS-MVSNet | | | 96.59 99 | 98.02 79 | 94.92 109 | 94.45 141 | 98.96 106 | 97.46 108 | 91.75 102 | 97.86 145 | 90.07 116 | 96.02 117 | 97.25 72 | 96.21 137 | 98.04 108 | 98.38 63 | 99.60 144 | 99.65 121 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpm | | | 92.38 189 | 94.79 156 | 89.56 204 | 94.30 142 | 97.50 190 | 94.24 196 | 78.97 222 | 97.72 151 | 74.93 213 | 97.97 70 | 82.91 187 | 96.60 130 | 93.65 214 | 94.81 197 | 98.33 206 | 98.98 180 |
|
Fast-Effi-MVS+ | | | 95.38 123 | 96.52 120 | 94.05 119 | 94.15 143 | 99.14 99 | 97.24 116 | 86.79 174 | 98.53 112 | 87.62 129 | 94.51 141 | 87.06 139 | 98.76 65 | 98.60 68 | 98.04 86 | 99.72 62 | 99.77 50 |
|
Effi-MVS+-dtu | | | 95.74 116 | 98.04 77 | 93.06 140 | 93.92 144 | 99.16 98 | 97.90 95 | 88.16 163 | 99.07 67 | 82.02 161 | 98.02 69 | 94.32 104 | 96.74 125 | 98.53 72 | 97.56 110 | 99.61 137 | 99.62 128 |
|
Fast-Effi-MVS+-dtu | | | 95.38 123 | 98.20 71 | 92.09 153 | 93.91 145 | 98.87 110 | 97.35 111 | 85.01 190 | 99.08 61 | 81.09 165 | 98.10 63 | 96.36 79 | 95.62 154 | 98.43 77 | 97.03 125 | 99.55 162 | 99.50 151 |
|
testpf | | | 91.80 204 | 94.43 165 | 88.74 207 | 93.89 146 | 95.30 223 | 92.05 209 | 71.77 234 | 97.52 155 | 87.24 130 | 94.77 139 | 92.68 120 | 91.48 214 | 91.75 226 | 92.11 223 | 96.02 231 | 96.89 216 |
|
TAMVS | | | 95.53 119 | 96.50 124 | 94.39 115 | 93.86 147 | 99.03 100 | 96.67 130 | 89.55 146 | 97.33 164 | 90.64 110 | 93.02 158 | 91.58 126 | 96.21 137 | 97.72 125 | 97.43 119 | 99.43 179 | 99.36 162 |
|
GBi-Net | | | 96.98 79 | 98.00 80 | 95.78 98 | 93.81 148 | 97.98 156 | 98.09 85 | 91.32 113 | 98.80 93 | 93.92 77 | 97.21 85 | 95.94 86 | 97.89 96 | 98.07 102 | 98.34 69 | 99.68 92 | 99.67 109 |
|
test1 | | | 96.98 79 | 98.00 80 | 95.78 98 | 93.81 148 | 97.98 156 | 98.09 85 | 91.32 113 | 98.80 93 | 93.92 77 | 97.21 85 | 95.94 86 | 97.89 96 | 98.07 102 | 98.34 69 | 99.68 92 | 99.67 109 |
|
FMVSNet2 | | | 96.64 96 | 97.50 92 | 95.63 104 | 93.81 148 | 97.98 156 | 98.09 85 | 90.87 119 | 98.99 73 | 93.48 85 | 93.17 154 | 95.25 91 | 97.89 96 | 98.63 64 | 98.80 40 | 99.68 92 | 99.67 109 |
|
MVS-HIRNet | | | 92.51 183 | 95.97 136 | 88.48 210 | 93.73 151 | 98.37 145 | 90.33 216 | 75.36 233 | 98.32 119 | 77.78 200 | 89.15 183 | 94.87 94 | 95.14 184 | 97.62 131 | 96.39 141 | 98.51 201 | 97.11 212 |
|
GA-MVS | | | 93.93 150 | 96.31 134 | 91.16 180 | 93.61 152 | 98.79 113 | 95.39 154 | 90.69 125 | 98.25 122 | 73.28 217 | 96.15 115 | 88.42 135 | 94.39 194 | 97.76 122 | 95.35 168 | 99.58 154 | 99.45 156 |
|
FC-MVSNet-test | | | 96.07 111 | 97.94 82 | 93.89 120 | 93.60 153 | 98.67 125 | 96.62 132 | 90.30 132 | 98.76 100 | 88.62 121 | 95.57 132 | 97.63 67 | 94.48 192 | 97.97 111 | 97.48 116 | 99.71 72 | 99.52 145 |
|
FMVSNet3 | | | 97.02 78 | 98.12 75 | 95.73 102 | 93.59 154 | 97.98 156 | 98.34 76 | 91.32 113 | 98.80 93 | 93.92 77 | 97.21 85 | 95.94 86 | 97.63 103 | 98.61 65 | 98.62 47 | 99.61 137 | 99.65 121 |
|
FMVSNet1 | | | 95.77 115 | 96.41 133 | 95.03 107 | 93.42 155 | 97.86 163 | 97.11 122 | 89.89 140 | 98.53 112 | 92.00 100 | 89.17 182 | 93.23 117 | 98.15 88 | 98.07 102 | 98.34 69 | 99.61 137 | 99.69 97 |
|
tfpnnormal | | | 93.85 153 | 94.12 170 | 93.54 131 | 93.22 156 | 98.24 151 | 95.45 152 | 91.96 100 | 94.61 220 | 83.91 144 | 90.74 165 | 81.75 205 | 97.04 115 | 97.49 135 | 96.16 149 | 99.68 92 | 99.84 20 |
|
TransMVSNet (Re) | | | 93.45 157 | 94.08 172 | 92.72 145 | 92.83 157 | 97.62 181 | 94.94 161 | 91.54 109 | 95.65 216 | 83.06 154 | 88.93 185 | 83.53 177 | 94.25 195 | 97.41 137 | 97.03 125 | 99.67 100 | 98.40 196 |
|
LTVRE_ROB | | 93.20 16 | 92.84 169 | 94.92 152 | 90.43 197 | 92.83 157 | 98.63 127 | 97.08 124 | 87.87 166 | 97.91 141 | 68.42 223 | 93.54 148 | 79.46 217 | 96.62 129 | 97.55 133 | 97.40 120 | 99.74 51 | 99.92 1 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
TESTMET0.1,1 | | | 94.95 131 | 97.32 100 | 92.20 150 | 92.62 159 | 98.74 120 | 96.44 137 | 86.67 176 | 98.18 125 | 82.75 156 | 96.60 104 | 94.67 99 | 95.54 157 | 98.09 99 | 96.00 151 | 99.20 192 | 98.93 182 |
|
pm-mvs1 | | | 94.27 142 | 95.57 146 | 92.75 144 | 92.58 160 | 98.13 154 | 94.87 167 | 90.71 124 | 96.70 188 | 83.78 146 | 89.94 177 | 89.85 133 | 94.96 187 | 97.58 132 | 97.07 124 | 99.61 137 | 99.72 85 |
|
NR-MVSNet | | | 94.01 146 | 94.51 162 | 93.44 133 | 92.56 161 | 97.77 164 | 95.67 146 | 91.57 107 | 97.17 169 | 85.84 138 | 93.13 155 | 80.53 210 | 95.29 180 | 97.01 149 | 96.17 148 | 99.69 83 | 99.75 65 |
|
EG-PatchMatch MVS | | | 92.45 184 | 93.92 181 | 90.72 192 | 92.56 161 | 98.43 141 | 94.88 166 | 84.54 194 | 97.18 168 | 79.55 188 | 86.12 217 | 83.23 181 | 93.15 208 | 97.22 143 | 96.00 151 | 99.67 100 | 99.27 166 |
|
test-mter | | | 94.86 132 | 97.32 100 | 92.00 157 | 92.41 163 | 98.82 112 | 96.18 141 | 86.35 180 | 98.05 131 | 82.28 159 | 96.48 108 | 94.39 103 | 95.46 167 | 98.17 92 | 96.20 147 | 99.32 187 | 99.13 176 |
|
our_test_3 | | | | | | 92.30 164 | 97.58 183 | 90.09 219 | | | | | | | | | | |
|
pmmvs4 | | | 95.09 128 | 95.90 138 | 94.14 117 | 92.29 165 | 97.70 170 | 95.45 152 | 90.31 130 | 98.60 106 | 90.70 108 | 93.25 152 | 89.90 132 | 96.67 127 | 97.13 146 | 95.42 165 | 99.44 177 | 99.28 165 |
|
FMVSNet5 | | | 95.42 121 | 96.47 125 | 94.20 116 | 92.26 166 | 95.99 210 | 95.66 147 | 87.15 170 | 97.87 143 | 93.46 86 | 96.68 100 | 93.79 112 | 97.52 104 | 97.10 148 | 97.21 123 | 99.11 195 | 96.62 221 |
|
UniMVSNet (Re) | | | 94.58 139 | 95.34 148 | 93.71 125 | 92.25 167 | 98.08 155 | 94.97 160 | 91.29 117 | 97.03 175 | 87.94 126 | 93.97 145 | 86.25 154 | 96.07 142 | 96.27 169 | 95.97 154 | 99.72 62 | 99.79 41 |
|
v18 | | | 92.63 181 | 93.67 187 | 91.43 171 | 92.13 168 | 95.65 211 | 95.09 157 | 85.44 185 | 97.06 173 | 80.78 167 | 90.06 170 | 83.06 182 | 95.47 166 | 95.16 194 | 95.01 183 | 99.64 122 | 99.67 109 |
|
v16 | | | 92.66 180 | 93.80 184 | 91.32 175 | 92.13 168 | 95.62 213 | 94.89 163 | 85.12 187 | 97.20 167 | 80.66 168 | 89.96 176 | 83.93 174 | 95.49 160 | 95.17 190 | 95.04 178 | 99.63 128 | 99.68 104 |
|
v17 | | | 92.55 182 | 93.65 188 | 91.27 177 | 92.11 170 | 95.63 212 | 94.89 163 | 85.15 186 | 97.12 172 | 80.39 176 | 90.02 171 | 83.02 183 | 95.45 168 | 95.17 190 | 94.92 193 | 99.66 105 | 99.68 104 |
|
SixPastTwentyTwo | | | 93.44 158 | 95.32 149 | 91.24 178 | 92.11 170 | 98.40 143 | 92.77 205 | 88.64 156 | 98.09 130 | 77.83 199 | 93.51 149 | 85.74 158 | 96.52 133 | 96.91 151 | 94.89 196 | 99.59 150 | 99.73 73 |
|
v8 | | | 92.87 168 | 93.87 183 | 91.72 168 | 92.05 172 | 97.50 190 | 94.79 174 | 88.20 161 | 96.85 184 | 80.11 179 | 90.01 172 | 82.86 189 | 95.48 161 | 95.15 198 | 94.90 194 | 99.66 105 | 99.80 34 |
|
v6 | | | 93.11 162 | 93.98 176 | 92.10 152 | 92.01 173 | 97.71 167 | 94.86 170 | 90.15 133 | 96.96 178 | 80.47 172 | 90.01 172 | 83.26 180 | 95.48 161 | 95.17 190 | 95.01 183 | 99.64 122 | 99.76 54 |
|
v1neww | | | 93.06 163 | 93.94 178 | 92.03 155 | 91.99 174 | 97.70 170 | 94.79 174 | 90.14 134 | 96.93 180 | 80.13 177 | 89.97 174 | 83.01 184 | 95.48 161 | 95.16 194 | 95.01 183 | 99.63 128 | 99.76 54 |
|
v7new | | | 93.06 163 | 93.94 178 | 92.03 155 | 91.99 174 | 97.70 170 | 94.79 174 | 90.14 134 | 96.93 180 | 80.13 177 | 89.97 174 | 83.01 184 | 95.48 161 | 95.16 194 | 95.01 183 | 99.63 128 | 99.76 54 |
|
WR-MVS_H | | | 93.54 156 | 94.67 158 | 92.22 148 | 91.95 176 | 97.91 161 | 94.58 190 | 88.75 153 | 96.64 192 | 83.88 145 | 90.66 167 | 85.13 165 | 94.40 193 | 96.54 159 | 95.91 156 | 99.73 57 | 99.89 7 |
|
V42 | | | 93.05 165 | 93.90 182 | 92.04 154 | 91.91 177 | 97.66 176 | 94.91 162 | 89.91 139 | 96.85 184 | 80.58 170 | 89.66 179 | 83.43 179 | 95.37 173 | 95.03 204 | 94.90 194 | 99.59 150 | 99.78 43 |
|
EU-MVSNet | | | 92.80 172 | 94.76 157 | 90.51 195 | 91.88 178 | 96.74 207 | 92.48 207 | 88.69 154 | 96.21 203 | 79.00 195 | 91.51 159 | 87.82 136 | 91.83 213 | 95.87 177 | 96.27 144 | 99.21 191 | 98.92 185 |
|
N_pmnet | | | 92.21 196 | 94.60 159 | 89.42 205 | 91.88 178 | 97.38 197 | 89.15 221 | 89.74 144 | 97.89 142 | 73.75 215 | 87.94 204 | 92.23 123 | 93.85 203 | 96.10 173 | 93.20 212 | 98.15 209 | 97.43 209 |
|
UniMVSNet_NR-MVSNet | | | 94.59 138 | 95.47 147 | 93.55 130 | 91.85 180 | 97.89 162 | 95.03 158 | 92.00 98 | 97.33 164 | 86.12 135 | 93.19 153 | 87.29 138 | 96.60 130 | 96.12 172 | 96.70 132 | 99.72 62 | 99.80 34 |
|
v15 | | | 92.27 194 | 93.33 199 | 91.04 182 | 91.83 181 | 95.60 214 | 94.79 174 | 84.88 191 | 96.66 190 | 79.66 186 | 88.72 190 | 82.45 196 | 95.40 171 | 95.19 189 | 95.00 187 | 99.65 111 | 99.67 109 |
|
v7 | | | 92.97 167 | 94.11 171 | 91.65 169 | 91.83 181 | 97.55 187 | 94.86 170 | 88.19 162 | 96.96 178 | 79.72 185 | 88.16 199 | 84.68 169 | 95.63 152 | 96.33 166 | 95.30 170 | 99.65 111 | 99.77 50 |
|
pmmvs6 | | | 91.90 203 | 92.53 214 | 91.17 179 | 91.81 183 | 97.63 178 | 93.23 201 | 88.37 159 | 93.43 225 | 80.61 169 | 77.32 229 | 87.47 137 | 94.12 197 | 96.58 156 | 95.72 160 | 98.88 199 | 99.53 143 |
|
V14 | | | 92.31 193 | 93.41 197 | 91.03 183 | 91.80 184 | 95.59 216 | 94.79 174 | 84.70 192 | 96.58 195 | 79.83 181 | 88.79 188 | 82.98 186 | 95.41 170 | 95.22 184 | 95.02 182 | 99.65 111 | 99.67 109 |
|
v1 | | | 92.81 170 | 93.57 192 | 91.94 159 | 91.79 185 | 97.70 170 | 94.80 173 | 90.32 128 | 96.52 198 | 79.75 183 | 88.47 195 | 82.46 195 | 95.32 177 | 95.14 200 | 94.96 190 | 99.63 128 | 99.73 73 |
|
v10 | | | 92.79 174 | 94.06 173 | 91.31 176 | 91.78 186 | 97.29 201 | 94.87 167 | 86.10 181 | 96.97 177 | 79.82 182 | 88.16 199 | 84.56 170 | 95.63 152 | 96.33 166 | 95.31 169 | 99.65 111 | 99.80 34 |
|
V9 | | | 92.24 195 | 93.32 201 | 90.98 185 | 91.76 187 | 95.58 218 | 94.83 172 | 84.50 196 | 96.68 189 | 79.73 184 | 88.66 191 | 82.39 197 | 95.39 172 | 95.22 184 | 95.03 180 | 99.65 111 | 99.67 109 |
|
v1141 | | | 92.79 174 | 93.61 189 | 91.84 165 | 91.75 188 | 97.71 167 | 94.74 180 | 90.33 127 | 96.58 195 | 79.21 193 | 88.59 192 | 82.53 194 | 95.36 174 | 95.16 194 | 94.96 190 | 99.63 128 | 99.72 85 |
|
divwei89l23v2f112 | | | 92.80 172 | 93.60 191 | 91.86 164 | 91.75 188 | 97.71 167 | 94.75 179 | 90.32 128 | 96.54 197 | 79.35 190 | 88.59 192 | 82.55 193 | 95.35 175 | 95.15 198 | 94.96 190 | 99.63 128 | 99.72 85 |
|
v13 | | | 92.16 198 | 93.28 203 | 90.85 190 | 91.75 188 | 95.58 218 | 94.65 187 | 84.23 200 | 96.49 201 | 79.51 189 | 88.40 197 | 82.58 192 | 95.31 179 | 95.21 187 | 95.03 180 | 99.66 105 | 99.68 104 |
|
MIMVSNet | | | 94.49 141 | 97.59 91 | 90.87 189 | 91.74 191 | 98.70 124 | 94.68 184 | 78.73 223 | 97.98 134 | 83.71 149 | 97.71 78 | 94.81 96 | 96.96 118 | 97.97 111 | 97.92 90 | 99.40 183 | 98.04 201 |
|
v11 | | | 92.43 186 | 93.77 185 | 90.85 190 | 91.72 192 | 95.58 218 | 94.87 167 | 84.07 202 | 96.98 176 | 79.28 191 | 88.03 202 | 84.22 173 | 95.53 159 | 96.55 158 | 95.36 167 | 99.65 111 | 99.70 91 |
|
v12 | | | 92.18 197 | 93.29 202 | 90.88 188 | 91.70 193 | 95.59 216 | 94.61 188 | 84.36 198 | 96.65 191 | 79.59 187 | 88.85 186 | 82.03 201 | 95.35 175 | 95.22 184 | 95.04 178 | 99.65 111 | 99.68 104 |
|
v1144 | | | 92.81 170 | 94.03 174 | 91.40 173 | 91.68 194 | 97.60 182 | 94.73 181 | 88.40 158 | 96.71 187 | 78.48 197 | 88.14 201 | 84.46 171 | 95.45 168 | 96.31 168 | 95.22 172 | 99.65 111 | 99.76 54 |
|
DU-MVS | | | 93.98 148 | 94.44 164 | 93.44 133 | 91.66 195 | 97.77 164 | 95.03 158 | 91.57 107 | 97.17 169 | 86.12 135 | 93.13 155 | 81.13 207 | 96.60 130 | 95.10 201 | 97.01 127 | 99.67 100 | 99.80 34 |
|
Baseline_NR-MVSNet | | | 93.87 151 | 93.98 176 | 93.75 123 | 91.66 195 | 97.02 202 | 95.53 150 | 91.52 110 | 97.16 171 | 87.77 128 | 87.93 205 | 83.69 175 | 96.35 135 | 95.10 201 | 97.23 122 | 99.68 92 | 99.73 73 |
|
CP-MVSNet | | | 93.25 160 | 94.00 175 | 92.38 147 | 91.65 197 | 97.56 185 | 94.38 193 | 89.20 148 | 96.05 208 | 83.16 153 | 89.51 180 | 81.97 202 | 96.16 141 | 96.43 161 | 96.56 137 | 99.71 72 | 99.89 7 |
|
v148 | | | 92.36 191 | 92.88 208 | 91.75 166 | 91.63 198 | 97.66 176 | 92.64 206 | 90.55 126 | 96.09 206 | 83.34 151 | 88.19 198 | 80.00 213 | 92.74 209 | 93.98 211 | 94.58 202 | 99.58 154 | 99.69 97 |
|
PS-CasMVS | | | 92.72 177 | 93.36 198 | 91.98 158 | 91.62 199 | 97.52 188 | 94.13 197 | 88.98 150 | 95.94 211 | 81.51 164 | 87.35 207 | 79.95 214 | 95.91 146 | 96.37 163 | 96.49 139 | 99.70 81 | 99.89 7 |
|
v2v482 | | | 92.77 176 | 93.52 196 | 91.90 162 | 91.59 200 | 97.63 178 | 94.57 191 | 90.31 130 | 96.80 186 | 79.22 192 | 88.74 189 | 81.55 206 | 96.04 144 | 95.26 183 | 94.97 189 | 99.66 105 | 99.69 97 |
|
v1192 | | | 92.43 186 | 93.61 189 | 91.05 181 | 91.53 201 | 97.43 194 | 94.61 188 | 87.99 164 | 96.60 193 | 76.72 205 | 87.11 209 | 82.74 190 | 95.85 147 | 96.35 165 | 95.30 170 | 99.60 144 | 99.74 69 |
|
WR-MVS | | | 93.43 159 | 94.48 163 | 92.21 149 | 91.52 202 | 97.69 174 | 94.66 186 | 89.98 138 | 96.86 183 | 83.43 150 | 90.12 169 | 85.03 166 | 93.94 201 | 96.02 175 | 95.82 157 | 99.71 72 | 99.82 26 |
|
v144192 | | | 92.38 189 | 93.55 195 | 91.00 184 | 91.44 203 | 97.47 193 | 94.27 194 | 87.41 169 | 96.52 198 | 78.03 198 | 87.50 206 | 82.65 191 | 95.32 177 | 95.82 178 | 95.15 174 | 99.55 162 | 99.78 43 |
|
pmmvs5 | | | 92.71 179 | 94.27 167 | 90.90 187 | 91.42 204 | 97.74 166 | 93.23 201 | 86.66 177 | 95.99 210 | 78.96 196 | 91.45 160 | 83.44 178 | 95.55 156 | 97.30 140 | 95.05 177 | 99.58 154 | 98.93 182 |
|
v1921920 | | | 92.36 191 | 93.57 192 | 90.94 186 | 91.39 205 | 97.39 196 | 94.70 183 | 87.63 168 | 96.60 193 | 76.63 206 | 86.98 210 | 82.89 188 | 95.75 148 | 96.26 170 | 95.14 175 | 99.55 162 | 99.73 73 |
|
gm-plane-assit | | | 89.44 214 | 92.82 212 | 85.49 217 | 91.37 206 | 95.34 222 | 79.55 232 | 82.12 205 | 91.68 228 | 64.79 229 | 87.98 203 | 80.26 212 | 95.66 151 | 98.51 73 | 97.56 110 | 99.45 175 | 98.41 194 |
|
v1240 | | | 91.99 200 | 93.33 199 | 90.44 196 | 91.29 207 | 97.30 200 | 94.25 195 | 86.79 174 | 96.43 202 | 75.49 211 | 86.34 215 | 81.85 204 | 95.29 180 | 96.42 162 | 95.22 172 | 99.52 168 | 99.73 73 |
|
PEN-MVS | | | 92.72 177 | 93.20 204 | 92.15 151 | 91.29 207 | 97.31 199 | 94.67 185 | 89.81 141 | 96.19 204 | 81.83 162 | 88.58 194 | 79.06 219 | 95.61 155 | 95.21 187 | 96.27 144 | 99.72 62 | 99.82 26 |
|
TranMVSNet+NR-MVSNet | | | 93.67 155 | 94.14 168 | 93.13 139 | 91.28 209 | 97.58 183 | 95.60 149 | 91.97 99 | 97.06 173 | 84.05 143 | 90.64 168 | 82.22 198 | 96.17 140 | 94.94 205 | 96.78 130 | 99.69 83 | 99.78 43 |
|
anonymousdsp | | | 93.12 161 | 95.86 140 | 89.93 203 | 91.09 210 | 98.25 150 | 95.12 156 | 85.08 188 | 97.44 156 | 73.30 216 | 90.89 164 | 90.78 129 | 95.25 182 | 97.91 114 | 95.96 155 | 99.71 72 | 99.82 26 |
|
MDTV_nov1_ep13_2view | | | 92.44 185 | 95.66 143 | 88.68 208 | 91.05 211 | 97.92 160 | 92.17 208 | 79.64 214 | 98.83 88 | 76.20 207 | 91.45 160 | 93.51 114 | 95.04 185 | 95.68 179 | 93.70 209 | 97.96 211 | 98.53 191 |
|
DTE-MVSNet | | | 92.42 188 | 92.85 210 | 91.91 161 | 90.87 212 | 96.97 203 | 94.53 192 | 89.81 141 | 95.86 213 | 81.59 163 | 88.83 187 | 77.88 222 | 95.01 186 | 94.34 210 | 96.35 142 | 99.64 122 | 99.73 73 |
|
V4 | | | 91.92 202 | 93.10 205 | 90.55 194 | 90.64 213 | 97.51 189 | 93.93 199 | 87.02 171 | 95.81 215 | 77.61 202 | 86.93 211 | 82.19 199 | 94.50 191 | 94.72 206 | 94.68 201 | 99.62 134 | 99.85 18 |
|
v52 | | | 91.94 201 | 93.10 205 | 90.57 193 | 90.62 214 | 97.50 190 | 93.98 198 | 87.02 171 | 95.86 213 | 77.67 201 | 86.93 211 | 82.16 200 | 94.53 190 | 94.71 207 | 94.70 200 | 99.61 137 | 99.85 18 |
|
v748 | | | 91.12 206 | 91.95 215 | 90.16 199 | 90.60 215 | 97.35 198 | 91.11 210 | 87.92 165 | 94.75 219 | 80.54 171 | 86.26 216 | 75.97 224 | 91.13 215 | 94.63 208 | 94.81 197 | 99.65 111 | 99.90 3 |
|
v7n | | | 91.61 205 | 92.95 207 | 90.04 200 | 90.56 216 | 97.69 174 | 93.74 200 | 85.59 183 | 95.89 212 | 76.95 204 | 86.60 214 | 78.60 221 | 93.76 204 | 97.01 149 | 94.99 188 | 99.65 111 | 99.87 13 |
|
test20.03 | | | 90.65 210 | 93.71 186 | 87.09 212 | 90.44 217 | 96.24 208 | 89.74 220 | 85.46 184 | 95.59 217 | 72.99 218 | 90.68 166 | 85.33 162 | 84.41 225 | 95.94 176 | 95.10 176 | 99.52 168 | 97.06 214 |
|
FPMVS | | | 83.82 221 | 84.61 227 | 82.90 222 | 90.39 218 | 90.71 230 | 90.85 214 | 84.10 201 | 95.47 218 | 65.15 227 | 83.44 220 | 74.46 226 | 75.48 228 | 81.63 232 | 79.42 234 | 91.42 235 | 87.14 233 |
|
Anonymous20231206 | | | 90.70 209 | 93.93 180 | 86.92 214 | 90.21 219 | 96.79 205 | 90.30 217 | 86.61 178 | 96.05 208 | 69.25 222 | 88.46 196 | 84.86 168 | 85.86 222 | 97.11 147 | 96.47 140 | 99.30 188 | 97.80 205 |
|
new_pmnet | | | 90.45 211 | 92.84 211 | 87.66 211 | 88.96 220 | 96.16 209 | 88.71 222 | 84.66 193 | 97.56 154 | 71.91 221 | 85.60 218 | 86.58 151 | 93.28 206 | 96.07 174 | 93.54 210 | 98.46 203 | 94.39 225 |
|
testus | | | 88.77 216 | 92.77 213 | 84.10 220 | 88.24 221 | 93.95 226 | 87.16 225 | 84.24 199 | 97.37 157 | 61.54 233 | 95.70 129 | 73.10 227 | 84.90 224 | 95.56 180 | 95.82 157 | 98.51 201 | 97.88 204 |
|
test2356 | | | 88.81 215 | 92.86 209 | 84.09 221 | 87.85 222 | 93.46 228 | 87.07 226 | 83.60 204 | 96.50 200 | 62.08 232 | 97.06 90 | 75.04 225 | 85.17 223 | 95.08 203 | 95.42 165 | 98.75 200 | 97.46 207 |
|
PM-MVS | | | 89.55 213 | 90.30 219 | 88.67 209 | 87.06 223 | 95.60 214 | 90.88 213 | 84.51 195 | 96.14 205 | 75.75 208 | 86.89 213 | 63.47 234 | 94.64 188 | 96.85 152 | 93.89 207 | 99.17 194 | 99.29 164 |
|
pmmvs-eth3d | | | 89.81 212 | 89.65 220 | 90.00 201 | 86.94 224 | 95.38 221 | 91.08 211 | 86.39 179 | 94.57 221 | 82.27 160 | 83.03 223 | 64.94 231 | 93.96 200 | 96.57 157 | 93.82 208 | 99.35 185 | 99.24 168 |
|
new-patchmatchnet | | | 86.12 220 | 87.30 222 | 84.74 218 | 86.92 225 | 95.19 225 | 83.57 229 | 84.42 197 | 92.67 226 | 65.66 226 | 80.32 226 | 64.72 232 | 89.41 217 | 92.33 222 | 89.21 228 | 98.43 204 | 96.69 219 |
|
pmmvs3 | | | 88.19 218 | 91.27 216 | 84.60 219 | 85.60 226 | 93.66 227 | 85.68 227 | 81.13 206 | 92.36 227 | 63.66 231 | 89.51 180 | 77.10 223 | 93.22 207 | 96.37 163 | 92.40 219 | 98.30 207 | 97.46 207 |
|
testmv | | | 81.83 223 | 86.26 223 | 76.66 226 | 84.10 227 | 89.42 233 | 74.29 236 | 79.65 213 | 90.61 229 | 51.85 238 | 82.11 224 | 63.06 236 | 72.61 231 | 91.94 224 | 92.75 214 | 97.49 219 | 93.94 227 |
|
test1235678 | | | 81.83 223 | 86.26 223 | 76.66 226 | 84.10 227 | 89.41 234 | 74.29 236 | 79.64 214 | 90.60 230 | 51.84 239 | 82.11 224 | 63.07 235 | 72.61 231 | 91.94 224 | 92.75 214 | 97.49 219 | 93.94 227 |
|
Gipuma | | | 81.40 225 | 81.78 228 | 80.96 224 | 83.21 229 | 85.61 238 | 79.73 231 | 76.25 231 | 97.33 164 | 64.21 230 | 55.32 235 | 55.55 238 | 86.04 221 | 92.43 221 | 92.20 222 | 96.32 230 | 93.99 226 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test12356 | | | 80.53 226 | 84.80 226 | 75.54 228 | 82.31 230 | 88.05 237 | 75.99 233 | 79.31 218 | 88.53 231 | 53.24 237 | 83.30 221 | 56.38 237 | 65.16 237 | 90.87 228 | 93.10 213 | 97.25 225 | 93.34 230 |
|
1111 | | | 82.87 222 | 85.67 225 | 79.62 225 | 81.86 231 | 89.62 231 | 74.44 234 | 68.81 236 | 87.44 233 | 66.59 224 | 76.83 230 | 70.33 229 | 87.71 219 | 92.65 217 | 93.37 211 | 98.28 208 | 89.42 231 |
|
.test1245 | | | 69.67 229 | 72.22 232 | 66.70 233 | 81.86 231 | 89.62 231 | 74.44 234 | 68.81 236 | 87.44 233 | 66.59 224 | 76.83 230 | 70.33 229 | 87.71 219 | 92.65 217 | 37.65 237 | 20.79 241 | 51.04 238 |
|
MDA-MVSNet-bldmvs | | | 87.84 219 | 89.22 221 | 86.23 215 | 81.74 233 | 96.77 206 | 83.74 228 | 89.57 145 | 94.50 222 | 72.83 219 | 96.64 102 | 64.47 233 | 92.71 210 | 81.43 233 | 92.28 221 | 96.81 228 | 98.47 193 |
|
MIMVSNet1 | | | 88.61 217 | 90.68 218 | 86.19 216 | 81.56 234 | 95.30 223 | 87.78 223 | 85.98 182 | 94.19 223 | 72.30 220 | 78.84 228 | 78.90 220 | 90.06 216 | 96.59 155 | 95.47 163 | 99.46 174 | 95.49 223 |
|
PMVS | | 72.60 17 | 76.39 228 | 77.66 231 | 74.92 229 | 81.04 235 | 69.37 243 | 68.47 239 | 80.54 210 | 85.39 235 | 65.07 228 | 73.52 232 | 72.91 228 | 65.67 236 | 80.35 234 | 76.81 235 | 88.71 237 | 85.25 237 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ambc | | | | 80.99 229 | | 80.04 236 | 90.84 229 | 90.91 212 | | 96.09 206 | 74.18 214 | 62.81 234 | 30.59 244 | 82.44 227 | 96.25 171 | 91.77 224 | 95.91 232 | 98.56 190 |
|
PMMVS2 | | | 77.26 227 | 79.47 230 | 74.70 230 | 76.00 237 | 88.37 236 | 74.22 238 | 76.34 229 | 78.31 236 | 54.13 235 | 69.96 233 | 52.50 239 | 70.14 234 | 84.83 231 | 88.71 229 | 97.35 221 | 93.58 229 |
|
EMVS | | | 68.12 232 | 68.11 234 | 68.14 232 | 75.51 238 | 71.76 241 | 55.38 242 | 77.20 228 | 77.78 237 | 37.79 242 | 53.59 236 | 43.61 240 | 74.72 229 | 67.05 238 | 76.70 236 | 88.27 239 | 86.24 235 |
|
E-PMN | | | 68.30 231 | 68.43 233 | 68.15 231 | 74.70 239 | 71.56 242 | 55.64 241 | 77.24 227 | 77.48 238 | 39.46 241 | 51.95 238 | 41.68 242 | 73.28 230 | 70.65 236 | 79.51 233 | 88.61 238 | 86.20 236 |
|
no-one | | | 66.79 233 | 67.62 235 | 65.81 234 | 73.06 240 | 81.79 239 | 51.90 244 | 76.20 232 | 61.07 240 | 54.05 236 | 51.62 239 | 41.72 241 | 49.18 238 | 67.26 237 | 82.83 232 | 90.47 236 | 87.07 234 |
|
MVE | | 67.97 19 | 65.53 234 | 67.43 236 | 63.31 235 | 59.33 241 | 74.20 240 | 53.09 243 | 70.43 235 | 66.27 239 | 43.13 240 | 45.98 240 | 30.62 243 | 70.65 233 | 79.34 235 | 86.30 230 | 83.25 240 | 89.33 232 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 31.24 235 | 40.15 237 | 20.86 237 | 12.61 242 | 17.99 244 | 25.16 245 | 13.30 239 | 48.42 241 | 24.82 243 | 53.07 237 | 30.13 245 | 28.47 239 | 42.73 239 | 37.65 237 | 20.79 241 | 51.04 238 |
|
test123 | | | 26.75 236 | 34.25 238 | 18.01 238 | 7.93 243 | 17.18 245 | 24.85 246 | 12.36 240 | 44.83 242 | 16.52 244 | 41.80 241 | 18.10 246 | 28.29 240 | 33.08 240 | 34.79 239 | 18.10 243 | 49.95 240 |
|
GG-mvs-BLEND | | | 69.11 230 | 98.13 74 | 35.26 236 | 3.49 244 | 98.20 153 | 94.89 163 | 2.38 241 | 98.42 117 | 5.82 245 | 96.37 111 | 98.60 56 | 5.97 241 | 98.75 54 | 97.98 88 | 99.01 196 | 98.61 189 |
|
sosnet-low-res | | | 0.00 237 | 0.00 239 | 0.00 239 | 0.00 245 | 0.00 246 | 0.00 247 | 0.00 242 | 0.00 243 | 0.00 246 | 0.00 242 | 0.00 247 | 0.00 242 | 0.00 241 | 0.00 240 | 0.00 244 | 0.00 241 |
|
sosnet | | | 0.00 237 | 0.00 239 | 0.00 239 | 0.00 245 | 0.00 246 | 0.00 247 | 0.00 242 | 0.00 243 | 0.00 246 | 0.00 242 | 0.00 247 | 0.00 242 | 0.00 241 | 0.00 240 | 0.00 244 | 0.00 241 |
|
MTAPA | | | | | | | | | | | 98.09 10 | | 99.97 4 | | | | | |
|
MTMP | | | | | | | | | | | 98.46 7 | | 99.96 10 | | | | | |
|
Patchmatch-RL test | | | | | | | | 66.86 240 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 98.57 109 | | | | | | | | |
|
Patchmtry | | | | | | | 98.59 130 | 97.15 119 | 79.14 219 | | 80.42 173 | | | | | | | |
|
DeepMVS_CX | | | | | | | 96.85 204 | 87.43 224 | 89.27 147 | 98.30 120 | 75.55 210 | 95.05 135 | 79.47 216 | 92.62 211 | 89.48 229 | | 95.18 233 | 95.96 222 |
|